You're a statistician who wants to transition into data science. What's the best way to make the move?

Every Statistician, by training, already has the entire Statistics background to work as a data scientist, tips for evolution: Go deeper into BI: Learn database theory, understand dimensional modeling, know how to cross tables of different types, good practices for building queries. . Programming: At least R or Python already has training, so go after SQL, Spark. Cloud: Learn at least one of the clouds (AWS, Microsoft, GCP). Scale data: Learn parallelization concepts, storage software, large-scale processing and real time analytics. Business: Master any and all contextual areas in which you work, speak the language of business and not just numbers. Communication: Communicate easily.

One thing that I have found useful while being a Data science masters student when I came across a lot of fellow students is that if you're a statistician who wants to transition into data science, the best way would be to brush up your Python, SQL, Excel, Power BI/Tableau or any other widely used data visualization tools as the statistics students/statisticians are already proficient in tools like R, IBM SPSS Statistics Editor, MATLAB etc.

A data scientist needs to be able to generate valuable business insights from data, insights that are often not visible to the naked eye. Being able to program in Python or R certainly helps with this: coding a statistical or machine learning model using TensorFlow is a great way to generate insights from ambiguous data. In addition, data visualization tools such as PowerBI or Tableau help communicate these insights clearly. However, some of the most important work I have done is using basic tools like SQL and Excel. If you think you know excel, no you don't (and neither do I!): it is an extremely powerful tool that takes a long time to master. Free online courses in all of these platforms will take you a long way.

As a statistician transitioning into data science, one can build a robust portfolio by focusing on practical projects that showcase relevant skills. Start by identifying areas of interest within data science, such as machine learning, data visualization, or predictive analytics. Then, embark on real-world projects utilizing datasets to demonstrate proficiency in various tools and techniques, like Python, R, SQL, and data manipulation libraries. Engaging in online courses, participating in hackathons, and contributing to open-source projects can also enhance expertise and credibility. Lastly, document projects on platforms like GitHub and LinkedIn, highlighting methodologies, insights, and the impact of data-driven solutions.

Transitioning from statistics to data science? Build a project portfolio showcasing your skills. Tackle datasets from your interest areas, applying data science tools for analysis and modeling. Participate in hackathons or online competitions to demonstrate your problem-solving abilities under constraints. Highlight in your portfolio not just technical expertise, but also creativity, problem-solving, and effective communication. This approach displays your practical application of data science principles, bridging the gap between statistics and the broader data science field.

Data science is a field that can be utilized in literally any other field. Sports? Try sports betting or score predicting. Food? Try trends in the culinary world or restaurant ops. Gaming? Try bot detection or game development. Banking? Try fraud or money laundering detection, or personalized marketing. No matter the field, build a portfolio that highlights how you think about data science and apply it to industries that don't conventionally use it. Datasets can be found from websites like Kaggle. And finally, don't forget to effectively communicate the results of your work.

Well-trained statisticians already have the core abilities to work as data scientists. In many cases, depending on college curricula, they would need supplemental training on ML/ non-parametric methods and to leverage programming skills. This could be quite polemical for some but IMHO statistics is still the core discipline of data science.

I like to think of data science as the intersection between business, programming, machine learning, and statistical knowledge. In order to become a data scientist, you need to know a bit of everything - experimental inference, statistical foundations, programming, industry expertise, knowledge of machine learning, data visualization - the list goes on. However, by taking just a couple of classes, a data scientist can easily acquire these core competencies as long as they are curious, understand the fundamentals of programming and statistics, and have fun learning.